Overview

Dataset statistics

Number of variables21
Number of observations1947
Missing cells5376
Missing cells (%)13.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory541.1 KiB
Average record size in memory284.6 B

Variable types

Categorical2
Numeric19

Alerts

Country has a high cardinality: 126 distinct valuesHigh cardinality
Country_Code has a high cardinality: 126 distinct valuesHigh cardinality
Total Agri Production is highly overall correlated with GDP(1000 USD) and 3 other fieldsHigh correlation
GDP(1000 USD) is highly overall correlated with Total Agri Production and 8 other fieldsHigh correlation
GDP per capita is highly overall correlated with GDP(1000 USD) and 7 other fieldsHigh correlation
No of frost days is highly overall correlated with Avg temperature and 1 other fieldsHigh correlation
Avg temperature is highly overall correlated with No of frost days and 4 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, both sexes (%) is highly overall correlated with GDP(1000 USD) and 7 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, female (%) is highly overall correlated with GDP(1000 USD) and 7 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, male (%) is highly overall correlated with GDP(1000 USD) and 7 other fieldsHigh correlation
Area is highly overall correlated with Total Agri Production and 2 other fieldsHigh correlation
Population is highly overall correlated with Total Agri Production and 3 other fieldsHigh correlation
Fertilizer Use Per Capita is highly overall correlated with GDP(1000 USD) and 6 other fieldsHigh correlation
Credit to Agriculture is highly overall correlated with Total Agri Production and 4 other fieldsHigh correlation
Agriculture share of Government Expenditure is highly overall correlated with GDP per capita and 3 other fieldsHigh correlation
Water Use Efficiency is highly overall correlated with GDP per capitaHigh correlation
Gini coefficient is highly overall correlated with No of frost days and 1 other fieldsHigh correlation
Agri_Prod_Per_Capita is highly overall correlated with GDP(1000 USD) and 5 other fieldsHigh correlation
Gross enrolment ratio, primary to tertiary, female (%) has 197 (10.1%) missing valuesMissing
Gross enrolment ratio, primary to tertiary, male (%) has 197 (10.1%) missing valuesMissing
Credit to Agriculture has 1558 (80.0%) missing valuesMissing
FDI inflows to Agriculture has 1027 (52.7%) missing valuesMissing
Agriculture share of Government Expenditure has 690 (35.4%) missing valuesMissing
Water Use Efficiency has 700 (36.0%) missing valuesMissing
Gini coefficient has 1007 (51.7%) missing valuesMissing
Total Agri Production has unique valuesUnique
GDP(1000 USD) has unique valuesUnique
GDP per capita has unique valuesUnique
Population has unique valuesUnique
Agri_Prod_Per_Capita has unique valuesUnique
No of frost days has 740 (38.0%) zerosZeros
Water Use Efficiency has 26 (1.3%) zerosZeros

Reproduction

Analysis started2023-11-03 02:53:12.889397
Analysis finished2023-11-03 02:53:56.018781
Duration43.13 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Country
Categorical

Distinct126
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size138.0 KiB
Indonesia
 
28
Italy
 
28
France
 
28
Finland
 
28
Sweden
 
28
Other values (121)
1807 

Length

Max length24
Median length18
Mean length7.5711351
Min length4

Characters and Unicode

Total characters14741
Distinct characters50
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowAlbania
2nd rowAlbania
3rd rowAlbania
4th rowAlbania
5th rowAlbania

Common Values

ValueCountFrequency (%)
Indonesia 28
 
1.4%
Italy 28
 
1.4%
France 28
 
1.4%
Finland 28
 
1.4%
Sweden 28
 
1.4%
Spain 28
 
1.4%
Norway 28
 
1.4%
Albania 27
 
1.4%
Portugal 27
 
1.4%
Ireland 27
 
1.4%
Other values (116) 1670
85.8%

Length

2023-11-02T19:53:56.132282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
indonesia 28
 
1.3%
france 28
 
1.3%
finland 28
 
1.3%
sweden 28
 
1.3%
spain 28
 
1.3%
norway 28
 
1.3%
italy 28
 
1.3%
cyprus 27
 
1.3%
switzerland 27
 
1.3%
slovenia 27
 
1.3%
Other values (131) 1856
87.0%

Most occurring characters

ValueCountFrequency (%)
a 2483
16.8%
i 1324
 
9.0%
n 1176
 
8.0%
e 967
 
6.6%
r 942
 
6.4%
o 733
 
5.0%
l 712
 
4.8%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.2%
Other values (40) 4847
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12422
84.3%
Uppercase Letter 2129
 
14.4%
Space Separator 186
 
1.3%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2483
20.0%
i 1324
10.7%
n 1176
9.5%
e 967
 
7.8%
r 942
 
7.6%
o 733
 
5.9%
l 712
 
5.7%
u 585
 
4.7%
t 495
 
4.0%
d 477
 
3.8%
Other values (16) 2528
20.4%
Uppercase Letter
ValueCountFrequency (%)
M 234
11.0%
S 206
 
9.7%
B 194
 
9.1%
C 185
 
8.7%
I 154
 
7.2%
A 143
 
6.7%
P 136
 
6.4%
N 119
 
5.6%
G 106
 
5.0%
F 103
 
4.8%
Other values (12) 549
25.8%
Space Separator
ValueCountFrequency (%)
186
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 14551
98.7%
Common 190
 
1.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2483
17.1%
i 1324
 
9.1%
n 1176
 
8.1%
e 967
 
6.6%
r 942
 
6.5%
o 733
 
5.0%
l 712
 
4.9%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.3%
Other values (38) 4657
32.0%
Common
ValueCountFrequency (%)
186
97.9%
- 4
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14741
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2483
16.8%
i 1324
 
9.0%
n 1176
 
8.0%
e 967
 
6.6%
r 942
 
6.4%
o 733
 
5.0%
l 712
 
4.8%
u 585
 
4.0%
t 495
 
3.4%
d 477
 
3.2%
Other values (40) 4847
32.9%

Year
Real number (ℝ)

Distinct29
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.5172
Minimum1991
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:56.256648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1992
Q11999
median2006
Q32012
95-th percentile2017
Maximum2019
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation7.9921652
Coefficient of variation (CV)0.0039850893
Kurtosis-1.106318
Mean2005.5172
Median Absolute Deviation (MAD)7
Skewness-0.15736562
Sum3904742
Variance63.874704
MonotonicityNot monotonic
2023-11-02T19:53:56.369106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2011 81
 
4.2%
2010 80
 
4.1%
2005 79
 
4.1%
2017 78
 
4.0%
2015 77
 
4.0%
2012 77
 
4.0%
2009 77
 
4.0%
2014 76
 
3.9%
2008 76
 
3.9%
2006 75
 
3.9%
Other values (19) 1171
60.1%
ValueCountFrequency (%)
1991 59
3.0%
1992 54
2.8%
1993 61
3.1%
1994 62
3.2%
1995 62
3.2%
1996 61
3.1%
1997 40
2.1%
1998 47
2.4%
1999 68
3.5%
2000 64
3.3%
ValueCountFrequency (%)
2019 23
 
1.2%
2018 68
3.5%
2017 78
4.0%
2016 75
3.9%
2015 77
4.0%
2014 76
3.9%
2013 72
3.7%
2012 77
4.0%
2011 81
4.2%
2010 80
4.1%

Total Agri Production
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44875692
Minimum296
Maximum3.5353873 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:56.494055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum296
5-th percentile184524.8
Q11712568
median7002967
Q321676264
95-th percentile1.078384 × 108
Maximum3.5353873 × 109
Range3.535387 × 109
Interquartile range (IQR)19963696

Descriptive statistics

Standard deviation2.2379089 × 108
Coefficient of variation (CV)4.9869067
Kurtosis129.77568
Mean44875692
Median Absolute Deviation (MAD)5982424
Skewness10.838162
Sum8.7372971 × 1010
Variance5.0082361 × 1016
MonotonicityNot monotonic
2023-11-02T19:53:56.651271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1885819 1
 
0.1%
5977448 1
 
0.1%
5998273 1
 
0.1%
6076422 1
 
0.1%
5401121 1
 
0.1%
5561801 1
 
0.1%
5029095 1
 
0.1%
4729117 1
 
0.1%
4501672 1
 
0.1%
4417239 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
296 1
0.1%
361 1
0.1%
12531 1
0.1%
15465 1
0.1%
16208 1
0.1%
16648 1
0.1%
18165 1
0.1%
18564 1
0.1%
20884 1
0.1%
21415 1
0.1%
ValueCountFrequency (%)
3535387313 1
0.1%
2925750697 1
0.1%
2841977977 1
0.1%
2840032663 1
0.1%
2835023231 1
0.1%
2775716224 1
0.1%
2766587295 1
0.1%
2662942827 1
0.1%
2619834639 1
0.1%
2032418918 1
0.1%

GDP(1000 USD)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0685466 × 108
Minimum586795.63
Maximum1.4698222 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:56.824344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum586795.63
5-th percentile1818154.8
Q19697771.5
median46658765
Q32.3972704 × 108
95-th percentile1.4710856 × 109
Maximum1.4698222 × 1010
Range1.4697635 × 1010
Interquartile range (IQR)2.3002927 × 108

Descriptive statistics

Standard deviation9.4354267 × 108
Coefficient of variation (CV)3.0748846
Kurtosis107.61935
Mean3.0685466 × 108
Median Absolute Deviation (MAD)43341446
Skewness9.0549583
Sum5.9744603 × 1011
Variance8.9027277 × 1017
MonotonicityNot monotonic
2023-11-02T19:53:57.029153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1700439.125 1
 
0.1%
8542083.076 1
 
0.1%
6640050.345 1
 
0.1%
5979106.917 1
 
0.1%
5890906.18 1
 
0.1%
5730380.115 1
 
0.1%
4713497.173 1
 
0.1%
4534418.924 1
 
0.1%
4330701.346 1
 
0.1%
3787172.382 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
586795.628 1
0.1%
591839.46 1
0.1%
628101.556 1
0.1%
639723.72 1
0.1%
654572.445 1
0.1%
662345.748 1
0.1%
679885.548 1
0.1%
706370.812 1
0.1%
726881.639 1
0.1%
729321.669 1
0.1%
ValueCountFrequency (%)
1.469822224 × 10101
0.1%
1.431192091 × 10101
0.1%
1.270220398 × 10101
0.1%
1.159924424 × 10101
0.1%
1.141600351 × 10101
0.1%
1.08219859 × 10101
0.1%
9897700345 1
0.1%
8838004954 1
0.1%
7836902033 1
0.1%
6344068421 1
0.1%

GDP per capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13792.068
Minimum94.466448
Maximum119940.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:57.170556image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum94.466448
5-th percentile331.62174
Q11541.1599
median5359.2391
Q319019.03
95-th percentile52633.895
Maximum119940.37
Range119845.9
Interquartile range (IQR)17477.87

Descriptive statistics

Standard deviation18754.519
Coefficient of variation (CV)1.3598047
Kurtosis5.5135113
Mean13792.068
Median Absolute Deviation (MAD)4757.7823
Skewness2.1882247
Sum26853157
Variance3.51732 × 108
MonotonicityNot monotonic
2023-11-02T19:53:57.327984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
515.173587 1
 
0.1%
422.642863 1
 
0.1%
258.539662 1
 
0.1%
236.028142 1
 
0.1%
236.051027 1
 
0.1%
233.326416 1
 
0.1%
206.877791 1
 
0.1%
203.286386 1
 
0.1%
198.703869 1
 
0.1%
178.074409 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
94.466448 1
0.1%
95.482892 1
0.1%
102.439454 1
0.1%
108.213069 1
0.1%
112.611423 1
0.1%
115.381036 1
0.1%
115.54302 1
0.1%
119.796791 1
0.1%
128.505986 1
0.1%
131.94554 1
0.1%
ValueCountFrequency (%)
119940.3682 1
0.1%
116793.6197 1
0.1%
112667.7211 1
0.1%
110752.3473 1
0.1%
110203.0082 1
0.1%
106581.887 1
0.1%
105455.8957 1
0.1%
102892.2834 1
0.1%
101514.3718 1
0.1%
100590.9564 1
0.1%

Country_Code
Categorical

Distinct126
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size129.3 KiB
IDN
 
28
ITA
 
28
REU
 
28
FIN
 
28
SWE
 
28
Other values (121)
1807 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters5841
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowALB
2nd rowALB
3rd rowALB
4th rowALB
5th rowALB

Common Values

ValueCountFrequency (%)
IDN 28
 
1.4%
ITA 28
 
1.4%
REU 28
 
1.4%
FIN 28
 
1.4%
SWE 28
 
1.4%
ESP 28
 
1.4%
NOR 28
 
1.4%
ALB 27
 
1.4%
PRT 27
 
1.4%
IRL 27
 
1.4%
Other values (116) 1670
85.8%

Length

2023-11-02T19:53:57.438546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
idn 28
 
1.4%
swe 28
 
1.4%
nor 28
 
1.4%
esp 28
 
1.4%
ita 28
 
1.4%
fin 28
 
1.4%
reu 28
 
1.4%
alb 27
 
1.4%
mex 27
 
1.4%
prt 27
 
1.4%
Other values (116) 1670
85.8%

Most occurring characters

ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5841
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 5841
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5841
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 552
 
9.5%
N 449
 
7.7%
A 446
 
7.6%
L 402
 
6.9%
S 324
 
5.5%
E 317
 
5.4%
T 312
 
5.3%
M 299
 
5.1%
U 286
 
4.9%
I 270
 
4.6%
Other values (16) 2184
37.4%

No of frost days
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct814
Distinct (%)41.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3223986
Minimum0
Maximum20.21
Zeros740
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:57.564620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.89
Q37.805
95-th percentile15.945
Maximum20.21
Range20.21
Interquartile range (IQR)7.805

Descriptive statistics

Standard deviation5.5455188
Coefficient of variation (CV)1.2829726
Kurtosis0.086561791
Mean4.3223986
Median Absolute Deviation (MAD)0.89
Skewness1.1123251
Sum8415.71
Variance30.752778
MonotonicityNot monotonic
2023-11-02T19:53:57.706624image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 740
38.0%
0.01 22
 
1.1%
0.02 14
 
0.7%
0.07 12
 
0.6%
0.04 10
 
0.5%
0.05 9
 
0.5%
0.13 7
 
0.4%
0.12 6
 
0.3%
0.21 5
 
0.3%
0.11 5
 
0.3%
Other values (804) 1117
57.4%
ValueCountFrequency (%)
0 740
38.0%
0.01 22
 
1.1%
0.02 14
 
0.7%
0.03 4
 
0.2%
0.04 10
 
0.5%
0.05 9
 
0.5%
0.06 5
 
0.3%
0.07 12
 
0.6%
0.08 5
 
0.3%
0.09 2
 
0.1%
ValueCountFrequency (%)
20.21 2
0.1%
20.04 1
0.1%
19.79 1
0.1%
19.77 1
0.1%
19.76 1
0.1%
19.74 1
0.1%
19.66 2
0.1%
19.58 1
0.1%
19.54 1
0.1%
19.48 1
0.1%

Precipitation
Real number (ℝ)

Distinct1844
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.388665
Minimum0.85
Maximum335.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:57.832417image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile9.578
Q152.85
median75.34
Q3118.42
95-th percentile248.644
Maximum335.77
Range334.92
Interquartile range (IQR)65.57

Descriptive statistics

Standard deviation64.82247
Coefficient of variation (CV)0.70162795
Kurtosis1.7183209
Mean92.388665
Median Absolute Deviation (MAD)32.05
Skewness1.3192265
Sum179880.73
Variance4201.9526
MonotonicityNot monotonic
2023-11-02T19:53:57.974137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.39 3
 
0.2%
66.85 3
 
0.2%
49.89 2
 
0.1%
151.24 2
 
0.1%
66.57 2
 
0.1%
44.36 2
 
0.1%
46.35 2
 
0.1%
126.71 2
 
0.1%
158.55 2
 
0.1%
62.18 2
 
0.1%
Other values (1834) 1925
98.9%
ValueCountFrequency (%)
0.85 1
0.1%
1.47 1
0.1%
1.78 1
0.1%
1.93 1
0.1%
2.21 1
0.1%
2.34 1
0.1%
2.93 1
0.1%
2.94 1
0.1%
3.12 1
0.1%
3.3 1
0.1%
ValueCountFrequency (%)
335.77 1
0.1%
325.68 1
0.1%
318.52 1
0.1%
313.86 1
0.1%
309.09 1
0.1%
308.63 1
0.1%
306.84 1
0.1%
306.06 1
0.1%
305.58 1
0.1%
303.97 1
0.1%

Avg temperature
Real number (ℝ)

Distinct1359
Distinct (%)69.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.400077
Minimum-4.96
Maximum29.78
Zeros0
Zeros (%)0.0%
Negative52
Negative (%)2.7%
Memory size30.4 KiB
2023-11-02T19:53:58.115730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4.96
5-th percentile1.97
Q19.34
median18.34
Q324.615
95-th percentile27.607
Maximum29.78
Range34.74
Interquartile range (IQR)15.275

Descriptive statistics

Standard deviation8.6651123
Coefficient of variation (CV)0.52835802
Kurtosis-1.0314779
Mean16.400077
Median Absolute Deviation (MAD)7.42
Skewness-0.3306468
Sum31930.95
Variance75.084171
MonotonicityNot monotonic
2023-11-02T19:53:58.241196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.37 7
 
0.4%
25.17 6
 
0.3%
23.98 6
 
0.3%
26.5 5
 
0.3%
20.81 5
 
0.3%
25.95 5
 
0.3%
23.62 5
 
0.3%
12.55 4
 
0.2%
25.82 4
 
0.2%
7.42 4
 
0.2%
Other values (1349) 1896
97.4%
ValueCountFrequency (%)
-4.96 1
0.1%
-4.88 1
0.1%
-4.79 1
0.1%
-4.4 1
0.1%
-4.38 1
0.1%
-4.36 1
0.1%
-4.35 1
0.1%
-4.34 1
0.1%
-4.33 1
0.1%
-4.26 1
0.1%
ValueCountFrequency (%)
29.78 1
0.1%
29.29 2
0.1%
29.14 1
0.1%
29.11 1
0.1%
29.09 1
0.1%
29.08 1
0.1%
29.06 1
0.1%
29.02 1
0.1%
29 1
0.1%
28.99 1
0.1%
Distinct1946
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.874749
Minimum13.86884
Maximum133.05758
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:58.371629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum13.86884
5-th percentile42.561947
Q167.29152
median77.84823
Q389.868985
95-th percentile101.82071
Maximum133.05758
Range119.18874
Interquartile range (IQR)22.577465

Descriptive statistics

Standard deviation18.34336
Coefficient of variation (CV)0.2386136
Kurtosis0.7030922
Mean76.874749
Median Absolute Deviation (MAD)11.35819
Skewness-0.61375974
Sum149675.14
Variance336.47887
MonotonicityNot monotonic
2023-11-02T19:53:58.493192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.28946 2
 
0.1%
61.45394 1
 
0.1%
59.4382 1
 
0.1%
59.69782 1
 
0.1%
55.74447 1
 
0.1%
56.83317 1
 
0.1%
60.95266 1
 
0.1%
58.42994 1
 
0.1%
58.16263 1
 
0.1%
58.05668 1
 
0.1%
Other values (1936) 1936
99.4%
ValueCountFrequency (%)
13.86884 1
0.1%
14.54958 1
0.1%
14.7984 1
0.1%
15.68368 1
0.1%
16.44042 1
0.1%
16.733 1
0.1%
17.16467 1
0.1%
17.20653 1
0.1%
17.80632 1
0.1%
18.42457 1
0.1%
ValueCountFrequency (%)
133.05758 1
0.1%
131.17207 1
0.1%
126.40767 1
0.1%
119.79754 1
0.1%
118.66204 1
0.1%
118.17177 1
0.1%
118.03144 1
0.1%
117.94248 1
0.1%
117.60128 1
0.1%
117.58541 1
0.1%

Gross enrolment ratio, primary to tertiary, female (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1750
Distinct (%)100.0%
Missing197
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean79.097094
Minimum10.61281
Maximum136.31571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:58.634642image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum10.61281
5-th percentile37.546173
Q168.835223
median80.85719
Q393.630935
95-th percentile106.71515
Maximum136.31571
Range125.7029
Interquartile range (IQR)24.795713

Descriptive statistics

Standard deviation20.997925
Coefficient of variation (CV)0.26547024
Kurtosis0.61567221
Mean79.097094
Median Absolute Deviation (MAD)12.41316
Skewness-0.70631833
Sum138419.91
Variance440.91284
MonotonicityNot monotonic
2023-11-02T19:53:58.776454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62.71862 1
 
0.1%
37.66979 1
 
0.1%
59.73169 1
 
0.1%
59.8672 1
 
0.1%
57.02117 1
 
0.1%
57.51932 1
 
0.1%
58.8317 1
 
0.1%
57.59219 1
 
0.1%
56.21647 1
 
0.1%
53.30564 1
 
0.1%
Other values (1740) 1740
89.4%
(Missing) 197
 
10.1%
ValueCountFrequency (%)
10.61281 1
0.1%
10.93612 1
0.1%
11.27414 1
0.1%
11.88241 1
0.1%
12.67699 1
0.1%
12.71298 1
0.1%
12.73484 1
0.1%
14.00474 1
0.1%
14.24069 1
0.1%
14.3452 1
0.1%
ValueCountFrequency (%)
136.31571 1
0.1%
134.26299 1
0.1%
129.83369 1
0.1%
127.47741 1
0.1%
124.85328 1
0.1%
124.68705 1
0.1%
124.07735 1
0.1%
123.93564 1
0.1%
123.59357 1
0.1%
123.47489 1
0.1%

Gross enrolment ratio, primary to tertiary, male (%)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1749
Distinct (%)99.9%
Missing197
Missing (%)10.1%
Infinite0
Infinite (%)0.0%
Mean77.022022
Minimum17.6678
Maximum129.9769
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:58.921664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum17.6678
5-th percentile47.747504
Q169.10571
median77.49625
Q387.847865
95-th percentile99.094519
Maximum129.9769
Range112.3091
Interquartile range (IQR)18.742155

Descriptive statistics

Standard deviation16.052658
Coefficient of variation (CV)0.20841647
Kurtosis0.95604157
Mean77.022022
Median Absolute Deviation (MAD)9.52197
Skewness-0.61867542
Sum134788.54
Variance257.68783
MonotonicityNot monotonic
2023-11-02T19:53:59.094103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83.68705 2
 
0.1%
60.942 1
 
0.1%
53.70084 1
 
0.1%
66.6072 1
 
0.1%
67.22742 1
 
0.1%
64.39357 1
 
0.1%
65.3709 1
 
0.1%
67.59567 1
 
0.1%
66.62554 1
 
0.1%
66.24506 1
 
0.1%
Other values (1739) 1739
89.3%
(Missing) 197
 
10.1%
ValueCountFrequency (%)
17.6678 1
0.1%
18.40578 1
0.1%
20.00603 1
0.1%
20.08472 1
0.1%
21.60872 1
0.1%
22.56948 1
0.1%
23.77303 1
0.1%
24.81132 1
0.1%
25.52446 1
0.1%
25.85483 1
0.1%
ValueCountFrequency (%)
129.9769 1
0.1%
128.23953 1
0.1%
123.14851 1
0.1%
119.07949 1
0.1%
116.90927 1
0.1%
114.34186 1
0.1%
113.93687 1
0.1%
113.76241 1
0.1%
112.81493 1
0.1%
112.60517 1
0.1%

Area
Real number (ℝ)

Distinct1692
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30374.974
Minimum0.66
Maximum529038.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:59.237414image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.66
5-th percentile66
Q11579.6553
median4995
Q327177.5
95-th percentile173642.61
Maximum529038.6
Range529037.94
Interquartile range (IQR)25597.845

Descriptive statistics

Standard deviation73578.445
Coefficient of variation (CV)2.4223377
Kurtosis25.411729
Mean30374.974
Median Absolute Deviation (MAD)4810
Skewness4.6884948
Sum59140075
Variance5.4137875 × 109
MonotonicityNot monotonic
2023-11-02T19:53:59.378938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.4 11
 
0.6%
10 10
 
0.5%
215494 10
 
0.5%
13 8
 
0.4%
40895 8
 
0.4%
1042 8
 
0.4%
1.55 7
 
0.4%
9 7
 
0.4%
38820 7
 
0.4%
4121 6
 
0.3%
Other values (1682) 1865
95.8%
ValueCountFrequency (%)
0.66 3
0.2%
1.5 2
 
0.1%
1.55 7
0.4%
2.4 1
 
0.1%
2.9 1
 
0.1%
3 1
 
0.1%
8 4
0.2%
8.11 1
 
0.1%
8.9 1
 
0.1%
9 7
0.4%
ValueCountFrequency (%)
529038.6 1
0.1%
528451.7556 1
0.1%
528280.8 1
0.1%
527861.9111 1
0.1%
527526.5 1
0.1%
527272.0667 1
0.1%
526768.8 1
0.1%
526013.5 1
0.1%
525260.8 1
0.1%
524505.5 1
0.1%

Population
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50737959
Minimum84534
Maximum1.4538015 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:59.505126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum84534
5-th percentile407482.7
Q13322607
median9259362
Q328206781
95-th percentile1.4879806 × 108
Maximum1.4538015 × 109
Range1.453717 × 109
Interquartile range (IQR)24884174

Descriptive statistics

Standard deviation1.8515789 × 108
Coefficient of variation (CV)3.6492971
Kurtosis39.947938
Mean50737959
Median Absolute Deviation (MAD)7296497
Skewness6.3011607
Sum9.8786806 × 1010
Variance3.4283443 × 1016
MonotonicityNot monotonic
2023-11-02T19:53:59.662677image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3300711 1
 
0.1%
20211114 1
 
0.1%
25682908 1
 
0.1%
25332178 1
 
0.1%
24956071 1
 
0.1%
24559500 1
 
0.1%
22783969 1
 
0.1%
22305571 1
 
0.1%
21794751 1
 
0.1%
21267359 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
84534 1
0.1%
85695 1
0.1%
86729 1
0.1%
87674 1
0.1%
89644 1
0.1%
91030 1
0.1%
92409 1
0.1%
93827 1
0.1%
95309 1
0.1%
96714 1
0.1%
ValueCountFrequency (%)
1453801543 1
0.1%
1448928199 1
0.1%
1442041109 1
0.1%
1433546767 1
0.1%
1425242661 1
0.1%
1416568531 1
0.1%
1407320843 1
0.1%
1397611702 1
0.1%
1387984919 1
0.1%
1383112050 1
0.1%

Fertilizer Use Per Capita
Real number (ℝ)

Distinct1682
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.020252
Minimum0
Maximum211.27
Zeros2
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:53:59.804374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.573
Q14.345
median16.26
Q335.88
95-th percentile85.36
Maximum211.27
Range211.27
Interquartile range (IQR)31.535

Descriptive statistics

Standard deviation30.912043
Coefficient of variation (CV)1.1879994
Kurtosis8.4694922
Mean26.020252
Median Absolute Deviation (MAD)13.6
Skewness2.513806
Sum50661.43
Variance955.55439
MonotonicityNot monotonic
2023-11-02T19:53:59.930035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 6
 
0.3%
2.21 5
 
0.3%
0.03 4
 
0.2%
1.8 4
 
0.2%
0.28 4
 
0.2%
0.99 4
 
0.2%
0.53 4
 
0.2%
0.33 4
 
0.2%
0.15 4
 
0.2%
1.75 4
 
0.2%
Other values (1672) 1904
97.8%
ValueCountFrequency (%)
0 2
 
0.1%
0.01 6
0.3%
0.02 1
 
0.1%
0.03 4
0.2%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.06 1
 
0.1%
0.08 1
 
0.1%
0.09 1
 
0.1%
0.11 2
 
0.1%
ValueCountFrequency (%)
211.27 1
0.1%
208.97 1
0.1%
201.94 1
0.1%
193.55 1
0.1%
190.35 1
0.1%
189.74 1
0.1%
189.33 1
0.1%
189.09 1
0.1%
188.35 1
0.1%
187.09 1
0.1%

Credit to Agriculture
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct388
Distinct (%)99.7%
Missing1558
Missing (%)80.0%
Infinite0
Infinite (%)0.0%
Mean5.5136162 × 109
Minimum22750
Maximum1.6745882 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:54:00.073366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum22750
5-th percentile2156122.8
Q124373000
median1.6396089 × 108
Q32.7529759 × 109
95-th percentile1.7522001 × 1010
Maximum1.6745882 × 1011
Range1.674588 × 1011
Interquartile range (IQR)2.7286029 × 109

Descriptive statistics

Standard deviation1.9348141 × 1010
Coefficient of variation (CV)3.5091563
Kurtosis36.371692
Mean5.5136162 × 109
Median Absolute Deviation (MAD)1.6142598 × 108
Skewness5.8260648
Sum2.1447967 × 1012
Variance3.7435056 × 1020
MonotonicityNot monotonic
2023-11-02T19:54:00.232099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
820313 2
 
0.1%
2322587105 1
 
0.1%
124425408 1
 
0.1%
105812944 1
 
0.1%
99313943 1
 
0.1%
89230399 1
 
0.1%
90011436 1
 
0.1%
91502705 1
 
0.1%
109863791 1
 
0.1%
63459980 1
 
0.1%
Other values (378) 378
 
19.4%
(Missing) 1558
80.0%
ValueCountFrequency (%)
22750 1
0.1%
22949 1
0.1%
110922 1
0.1%
193377 1
0.1%
548755 1
0.1%
676187 1
0.1%
820313 2
0.1%
872220 1
0.1%
968685 1
0.1%
1251219 1
0.1%
ValueCountFrequency (%)
1.67458821 × 10111
0.1%
1.52580569 × 10111
0.1%
1.299489373 × 10111
0.1%
1.264921285 × 10111
0.1%
1.183343021 × 10111
0.1%
1.153926557 × 10111
0.1%
9.878236343 × 10101
0.1%
8.770622482 × 10101
0.1%
8.535623139 × 10101
0.1%
6.393300178 × 10101
0.1%
Distinct902
Distinct (%)98.0%
Missing1027
Missing (%)52.7%
Infinite0
Infinite (%)0.0%
Mean66021125
Minimum-1.2093895 × 109
Maximum4.93221 × 109
Zeros0
Zeros (%)0.0%
Negative154
Negative (%)7.9%
Memory size30.4 KiB
2023-11-02T19:54:00.984653image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-1.2093895 × 109
5-th percentile-22039910
Q1599933
median8540000
Q337597822
95-th percentile3.0424345 × 108
Maximum4.93221 × 109
Range6.1415995 × 109
Interquartile range (IQR)36997889

Descriptive statistics

Standard deviation3.2205558 × 108
Coefficient of variation (CV)4.8780687
Kurtosis115.05364
Mean66021125
Median Absolute Deviation (MAD)9672020
Skewness9.4249479
Sum6.0739435 × 1010
Variance1.037198 × 1017
MonotonicityNot monotonic
2023-11-02T19:54:01.137138image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 5
 
0.3%
12 3
 
0.2%
500000 2
 
0.1%
2000000 2
 
0.1%
60000 2
 
0.1%
304000000 2
 
0.1%
24000000 2
 
0.1%
13 2
 
0.1%
1000000 2
 
0.1%
8000000 2
 
0.1%
Other values (892) 896
46.0%
(Missing) 1027
52.7%
ValueCountFrequency (%)
-1209389531 1
0.1%
-972491668 1
0.1%
-661112651 1
0.1%
-425491573 1
0.1%
-419391308 1
0.1%
-400034541 1
0.1%
-382105017 1
0.1%
-366852658 1
0.1%
-334451348 1
0.1%
-273647000 1
0.1%
ValueCountFrequency (%)
4932210000 1
0.1%
4157780000 1
0.1%
3632120000 1
0.1%
3141160000 1
0.1%
2345480000 1
0.1%
2016398000 1
0.1%
1967640000 1
0.1%
1346195657 1
0.1%
1282000000 1
0.1%
1235222379 1
0.1%

Agriculture share of Government Expenditure
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct537
Distinct (%)42.7%
Missing690
Missing (%)35.4%
Infinite0
Infinite (%)0.0%
Mean2.7681066
Minimum0.09
Maximum24.49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:54:01.276899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.09
5-th percentile0.348
Q11.13
median2.01
Q33.48
95-th percentile7.644
Maximum24.49
Range24.4
Interquartile range (IQR)2.35

Descriptive statistics

Standard deviation2.6837228
Coefficient of variation (CV)0.96951571
Kurtosis11.24599
Mean2.7681066
Median Absolute Deviation (MAD)1.07
Skewness2.7973734
Sum3479.51
Variance7.2023682
MonotonicityNot monotonic
2023-11-02T19:54:01.418471image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.69 10
 
0.5%
0.95 8
 
0.4%
1.3 8
 
0.4%
1.65 8
 
0.4%
2.32 7
 
0.4%
0.71 7
 
0.4%
0.74 7
 
0.4%
1.35 7
 
0.4%
2.14 7
 
0.4%
1.54 7
 
0.4%
Other values (527) 1181
60.7%
(Missing) 690
35.4%
ValueCountFrequency (%)
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 2
 
0.1%
0.12 3
0.2%
0.15 1
 
0.1%
0.16 3
0.2%
0.18 6
0.3%
0.19 3
0.2%
0.2 1
 
0.1%
0.21 1
 
0.1%
ValueCountFrequency (%)
24.49 1
0.1%
19.85 1
0.1%
17.75 1
0.1%
17.11 1
0.1%
16.37 1
0.1%
16.3 1
0.1%
16.16 1
0.1%
15.96 1
0.1%
15.93 1
0.1%
15.61 1
0.1%

Water Use Efficiency
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct273
Distinct (%)21.9%
Missing700
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean0.70907779
Minimum0
Maximum13.01
Zeros26
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:54:01.544507image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.02
Q10.085
median0.25
Q30.63
95-th percentile3.377
Maximum13.01
Range13.01
Interquartile range (IQR)0.545

Descriptive statistics

Standard deviation1.2518584
Coefficient of variation (CV)1.765474
Kurtosis16.896145
Mean0.70907779
Median Absolute Deviation (MAD)0.2
Skewness3.5392985
Sum884.22
Variance1.5671495
MonotonicityNot monotonic
2023-11-02T19:54:01.685978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 54
 
2.8%
0.03 54
 
2.8%
0.04 46
 
2.4%
0.06 35
 
1.8%
0.18 34
 
1.7%
0.02 31
 
1.6%
0 26
 
1.3%
0.08 26
 
1.3%
0.19 23
 
1.2%
0.1 23
 
1.2%
Other values (263) 895
46.0%
(Missing) 700
36.0%
ValueCountFrequency (%)
0 26
1.3%
0.01 19
 
1.0%
0.02 31
1.6%
0.03 54
2.8%
0.04 46
2.4%
0.05 54
2.8%
0.06 35
1.8%
0.07 21
 
1.1%
0.08 26
1.3%
0.09 21
 
1.1%
ValueCountFrequency (%)
13.01 1
0.1%
10.22 1
0.1%
7.51 1
0.1%
7.27 1
0.1%
7.06 1
0.1%
6.95 1
0.1%
6.87 1
0.1%
6.74 1
0.1%
6.64 1
0.1%
6.63 1
0.1%

Gini coefficient
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct940
Distinct (%)100.0%
Missing1007
Missing (%)51.7%
Infinite0
Infinite (%)0.0%
Mean0.36109006
Minimum0.22879762
Maximum0.6331876
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:54:01.811730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.22879762
5-th percentile0.26170957
Q10.29990604
median0.33867104
Q30.40236861
95-th percentile0.53264931
Maximum0.6331876
Range0.40438998
Interquartile range (IQR)0.10246256

Descriptive statistics

Standard deviation0.08281919
Coefficient of variation (CV)0.22935882
Kurtosis0.20887658
Mean0.36109006
Median Absolute Deviation (MAD)0.04723214
Skewness0.948974
Sum339.42466
Variance0.0068590182
MonotonicityNot monotonic
2023-11-02T19:54:01.953628image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4063700845 1
 
0.1%
0.4893605282 1
 
0.1%
0.4988384766 1
 
0.1%
0.4721390937 1
 
0.1%
0.4871346605 1
 
0.1%
0.4872243904 1
 
0.1%
0.4767771275 1
 
0.1%
0.467069553 1
 
0.1%
0.331953402 1
 
0.1%
0.3027196285 1
 
0.1%
Other values (930) 930
47.8%
(Missing) 1007
51.7%
ValueCountFrequency (%)
0.2287976171 1
0.1%
0.2295589642 1
0.1%
0.2345913699 1
0.1%
0.2371801896 1
0.1%
0.2376631971 1
0.1%
0.2402536591 1
0.1%
0.2404288658 1
0.1%
0.2412150077 1
0.1%
0.2421930735 1
0.1%
0.2435475319 1
0.1%
ValueCountFrequency (%)
0.6331875979 1
0.1%
0.6324036513 1
0.1%
0.6302607273 1
0.1%
0.613307461 1
0.1%
0.6079065038 1
0.1%
0.5873960008 1
0.1%
0.5868289301 1
0.1%
0.5821961738 1
0.1%
0.5815903937 1
0.1%
0.5811364313 1
0.1%

Agri_Prod_Per_Capita
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1947
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean875.38899
Minimum1.0127137
Maximum4690.0415
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size30.4 KiB
2023-11-02T19:54:02.095495image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.0127137
5-th percentile139.98234
Q1405.85494
median728.68936
Q31200.1201
95-th percentile2146.9748
Maximum4690.0415
Range4689.0288
Interquartile range (IQR)794.26512

Descriptive statistics

Standard deviation635.68583
Coefficient of variation (CV)0.72617526
Kurtosis2.3406303
Mean875.38899
Median Absolute Deviation (MAD)358.42771
Skewness1.3682678
Sum1704382.4
Variance404096.47
MonotonicityNot monotonic
2023-11-02T19:54:02.212047image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
571.3372058 1
 
0.1%
295.750546 1
 
0.1%
233.5511617 1
 
0.1%
239.8697025 1
 
0.1%
216.4251336 1
 
0.1%
226.4623058 1
 
0.1%
220.7295401 1
 
0.1%
212.0150612 1
 
0.1%
206.5484483 1
 
0.1%
207.7004014 1
 
0.1%
Other values (1937) 1937
99.5%
ValueCountFrequency (%)
1.012713662 1
0.1%
1.214564002 1
0.1%
8.984165916 1
0.1%
12.1046208 1
0.1%
13.97824933 1
0.1%
14.88768916 1
0.1%
16.49928667 1
0.1%
17.56364592 1
0.1%
20.93958286 1
0.1%
21.70491145 1
0.1%
ValueCountFrequency (%)
4690.041525 1
0.1%
3650.151094 1
0.1%
3593.730364 1
0.1%
3587.088299 1
0.1%
3549.855931 1
0.1%
3435.208578 1
0.1%
3413.282865 1
0.1%
3380.458142 1
0.1%
3325.858827 1
0.1%
3239.049977 1
0.1%

Interactions

2023-11-02T19:53:53.046157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:14.505674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:16.711965image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:18.807954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:20.935433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:22.968782image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:24.796866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:26.791078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:28.585529image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.453730image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.468249image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.217463image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.061588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.261276image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:40.982778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.193096image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.400142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:47.736060image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.559195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.154337image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:14.726574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:16.857438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:18.926999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.031462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.073056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:24.888716image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:26.882365image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:28.681119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.543668image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.551998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.320010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.183496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.371127image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.092648image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.296049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.538709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:47.854925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.687186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.264363image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:14.869232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:16.981340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:19.059767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.122940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.169162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:24.979669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:26.977039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:28.815299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.641064image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.646441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.421381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.302708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.480998image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.205219image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.411039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.633112image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:47.972229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.825213image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.389952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:15.002882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:17.071644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:19.195000image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.225737image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.257214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:25.072330image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:27.065039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:28.925816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.737159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.741233image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.529005image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.410015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.610577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.298567image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.552559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.733302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:48.098168image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.968539image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.515995image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:15.156464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:17.167434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:19.289502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.310852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.353009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:25.165962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:27.158171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:29.022462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.827137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.819376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.607382image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.501485image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:39.094044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.424859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.666574image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.851573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:48.208111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:51.093936image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.645657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:15.279699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:17.269531image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:19.384619image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.396939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.460848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:25.291617image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:27.265553image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:29.119620image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.924078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.918583image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.706523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.611170image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:39.283772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.566536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.804077image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.987888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:48.319194image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:51.237605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:53.754984image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:15.405767image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:17.364189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:19.478773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:21.519293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:23.561087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:25.403297image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:27.365116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:29.211083image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:31.013954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:33.011209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.789700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:36.711349image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:39.409615image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:41.708124image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.945327image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:46.132640image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:48.429053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
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2023-11-02T19:53:30.247634image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.267803image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:33.980793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:35.827003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.009674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:40.749078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:42.983667image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.161443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:47.500367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.322049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:52.776961image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:55.153444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:16.630817image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:18.705002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:20.828644image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:22.874087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:24.708466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:26.709554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:28.480483image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:30.370066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:32.379893image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:34.097439image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:35.954599image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:38.151289image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:40.883050image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:43.108175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:45.282470image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:47.625864image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:50.447565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-11-02T19:53:52.907649image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-11-02T19:54:02.328111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
YearTotal Agri ProductionGDP(1000 USD)GDP per capitaNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
Year1.0000.1630.2350.266-0.0570.0460.0860.3330.2990.278-0.0600.0480.0080.1820.132-0.2180.179-0.0740.307
Total Agri Production0.1631.0000.7950.0740.1220.034-0.0910.1740.1310.2110.7200.8990.3310.8020.316-0.117-0.0110.0390.460
GDP(1000 USD)0.2350.7951.0000.5820.281-0.023-0.2660.5290.5180.5470.4290.6300.5450.9110.252-0.4360.276-0.1730.522
GDP per capita0.2660.0740.5821.0000.315-0.056-0.3900.8040.8040.775-0.303-0.2150.5740.3610.015-0.6390.541-0.4590.606
No of frost days-0.0570.1220.2810.3151.000-0.201-0.9290.4520.4550.4600.047-0.0290.4370.348-0.049-0.1770.038-0.5680.314
Precipitation0.0460.034-0.023-0.056-0.2011.0000.114-0.022-0.0350.009-0.1260.0230.0710.0820.0480.0890.1120.3410.023
Avg temperature0.086-0.091-0.266-0.390-0.9290.1141.000-0.524-0.518-0.5370.0450.098-0.499-0.3820.0670.239-0.0720.603-0.401
Gross enrolment ratio, primary to tertiary, both sexes (%)0.3330.1740.5290.8040.452-0.022-0.5241.0000.9860.986-0.156-0.1050.6040.3830.030-0.5830.320-0.4490.622
Gross enrolment ratio, primary to tertiary, female (%)0.2990.1310.5180.8040.455-0.035-0.5180.9861.0000.951-0.179-0.1250.6000.3980.014-0.5730.316-0.4620.566
Gross enrolment ratio, primary to tertiary, male (%)0.2780.2110.5470.7750.4600.009-0.5370.9860.9511.000-0.122-0.0500.5930.4030.020-0.5800.289-0.4360.618
Area-0.0600.7200.429-0.3030.047-0.1260.045-0.156-0.179-0.1221.0000.8130.0650.5590.2680.158-0.3700.2060.011
Population0.0480.8990.630-0.215-0.0290.0230.098-0.105-0.125-0.0500.8131.0000.0690.6770.2760.080-0.1590.1590.073
Fertilizer Use Per Capita0.0080.3310.5450.5740.4370.071-0.4990.6040.6000.5930.0650.0691.0000.6290.065-0.3330.112-0.3130.637
Credit to Agriculture0.1820.8020.9110.3610.3480.082-0.3820.3830.3980.4030.5590.6770.6291.0000.228-0.1410.140-0.2270.414
FDI inflows to Agriculture0.1320.3160.2520.015-0.0490.0480.0670.0300.0140.0200.2680.2760.0650.2281.000-0.025-0.1340.0850.138
Agriculture share of Government Expenditure-0.218-0.117-0.436-0.639-0.1770.0890.239-0.583-0.573-0.5800.1580.080-0.333-0.141-0.0251.000-0.4020.160-0.433
Water Use Efficiency0.179-0.0110.2760.5410.0380.112-0.0720.3200.3160.289-0.370-0.1590.1120.140-0.134-0.4021.000-0.2480.211
Gini coefficient-0.0740.039-0.173-0.459-0.5680.3410.603-0.449-0.462-0.4360.2060.159-0.313-0.2270.0850.160-0.2481.000-0.239
Agri_Prod_Per_Capita0.3070.4600.5220.6060.3140.023-0.4010.6220.5660.6180.0110.0730.6370.4140.138-0.4330.211-0.2391.000

Missing values

2023-11-02T19:53:55.326484image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-02T19:53:55.625303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-02T19:53:55.861099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearTotal Agri ProductionGDP(1000 USD)GDP per capitaCountry_CodeNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
0Albania199318858191700439.125515.173587ALB6.2990.1812.1461.8122362.7186260.942001126.03300711.05.42NaNNaNNaNNaNNaN571.337206
1Albania199418249781880950.821571.023495ALB3.9576.6913.2162.0658561.8574562.268131126.03293999.04.48NaNNaNNaNNaNNaN554.031134
2Albania199514649742392764.887728.532187ALB5.08116.0311.8162.8797361.8349563.898441127.03284364.03.34NaNNaNNaNNaNNaN446.044957
3Albania199616021483199642.024978.085686ALB4.80123.9611.9163.4055762.5884264.197501131.03271331.01.99NaNNaNNaNNaN0.270103489.754170
4Albania199714319832224654.241683.726604ALB5.3881.0012.1265.1787164.1499266.187341135.03253719.01.60NaNNaNNaNNaNNaN440.106537
5Albania199815274172554868.837790.448796ALB5.48103.4712.4166.26195NaNNaN1139.03232175.07.73NaNNaNNaNNaNNaN472.566306
6Albania199915077593221670.1651004.179887ALB4.88107.2712.7966.3949965.6227567.152451145.03208260.03.30NaNNaNNaNNaNNaN469.961599
7Albania200015489583487586.3021096.028688ALB5.5881.5612.5665.9646165.4222466.495421144.03182021.05.87NaNNaNNaNNaNNaN486.784342
8Albania200115740633926887.5971245.203150ALB4.8596.1912.5466.3658566.1895166.538721139.03153612.05.93NaNNaN2.930.26NaN499.130204
9Albania200216584844355865.8891394.523697ALB3.91104.9112.6065.96581NaNNaN1140.03123551.018.06NaNNaN3.720.370.317390530.961076
CountryYearTotal Agri ProductionGDP(1000 USD)GDP per capitaCountry_CodeNo of frost daysPrecipitationAvg temperatureGross enrolment ratio, primary to tertiary, both sexes (%)Gross enrolment ratio, primary to tertiary, female (%)Gross enrolment ratio, primary to tertiary, male (%)AreaPopulationFertilizer Use Per CapitaCredit to AgricultureFDI inflows to AgricultureAgriculture share of Government ExpenditureWater Use EfficiencyGini coefficientAgri_Prod_Per_Capita
1937Uruguay2007537550423410566.867033.049382URY0.41113.5017.1388.49207NaNNaN14550.03328651.061.8620068104.0335020000.01.150.170.4640511614.919678
1938Uruguay2008735718330366193.339102.232149URY0.0768.7917.7986.58782NaNNaN14674.03336126.057.1422974252.0600982000.01.200.180.4508182205.307294
1939Uruguay2010828609840284556.6512015.732222URY0.30103.4617.3790.51878NaNNaN14433.03352651.088.4032190854.0314364000.01.010.160.4447502471.506280
1940Uruguay20131095181159963430.3017734.468529URY0.1790.4717.2595.11336NaNNaN14346.73381180.0172.1658885727.0342442549.00.940.230.4045093239.049977
1941Uruguay20141128018960484771.7817833.372482URY0.08133.9917.8195.68494NaNNaN14475.13391662.0132.5263197725.043211760.00.990.220.4011113325.858827
1942Uruguay2015883354857080759.0916774.555410URY0.20103.8217.8895.61326NaNNaN14467.63402818.082.6148841471.042023232.00.910.210.4012882595.950768
1943Uruguay2016806777557236652.4916766.425259URY0.05111.8817.2597.38798NaNNaN14265.33413766.099.9045199938.0182055671.00.940.210.3969552363.306389
1944Uruguay2017894316964233966.8618769.787523URY0.08113.8418.4298.66813NaNNaN14222.93422200.0100.3245811604.0-88928363.00.880.170.3946452613.280638
1945Zimbabwe2012182428617114849.881290.193956ZWE0.0150.6521.8066.5138664.4611568.6440416200.013265331.05.50NaNNaN5.670.04NaN137.522841
1946Zimbabwe2013119727419091019.991408.367810ZWE0.0066.4221.5966.2462164.4088368.1471916200.013555422.07.56NaNNaNNaN0.04NaN88.324362